Generative AI - An opportunity for enhancement

Most people would agree that generative AI will be (already is) the most significant global disruptive force.?

Sharing some views on Generative AI as we are moving towards implementing/applying ?Generative AI algorithms/tools in various fields of activities and in developing solutions to business challenges, and enhancing consumer/citizen friendly services

As we know, ‘Generative AI’ is a kind of artificial intelligence that creates new content, including text, images, audio, and video, based on patterns it has learned from existing content.

It is important to build trustworthy generative AI and to be seen as an opportunity as enhancer similar to application of mobile technology over past several years as we know Mobile technology and the Internet have become the main driving forces for the development of information and communication technologies.?Generative AI is another phase in the technology evolution and is becoming a key disruptor. ?Generative AI is not about replacing humans but rather augmenting human capabilities and expanding possibilities. While the privacy concerns surrounding AI is the potential for data breaches and unauthorized access to personal information – it is important to consider these aspects in training the models while applying appropriate constraints before implementing Generative AI solutions.

Employees should be encouraged to upskill and reskill to work alongside AI systems, envisioning a future where humans and AI collaborate harmoniously for growth in enhancing and exploring new possibilities

Here are some of the evolving principles of Generative AI:

1. Learning from Data: At its heart, Generative AI involves training on vast amounts of data, allowing the model to learn patterns, structures, and nuances.
2. Probability & Distribution: Generative models often estimate the probability distribution of the training data, making them capable of generating samples from that distribution.
3. Variability: Generative AI doesn't just replicate existing samples; it creates new, varied content based on the patterns it has learned.
4. Latent Spaces: Many generative models, like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), use latent spaces. These are compressed representations of data where similar data points are close together. By sampling and manipulating points in this space, new content can be generated.
5. Generative vs. Discriminative Modelling: While discriminative models differentiate between categories (e.g., is this a cat or a mouse?), generative models model the distribution of individual categories (e.g., how does a mouse generally look like?).
6. Adversarial Training: This is specific to GANs(Generative Adversarial Networks - power class of neural networks).??The generative model (generator) and a discriminative model (discriminator) are trained together in a game-theoretical manner. The generator tries to produce fake data that looks real, while the discriminator tries to distinguish between real and fake.
7. Regularization and Control: Generative models can sometimes produce unrealistic outputs. Techniques are often applied to ensure generated content is within desired bounds or adheres to certain constraints
8. Transfer Learning & Fine-tuning: Similar to other AI models, generative models can benefit from pre-training on large datasets and then fine-tuning on specific, smaller datasets.
9. Evaluation Challenges: Quantifying how "good" a generative model is can be challenging.?Traditional accuracy metrics might not apply. Novel metrics, like Frechet Inception Distance (FID) for GANs, have been developed to address this.?The FID can be used to evaluate generative models by calculating the FID between real and fake data distributions (lower is better)
10. Ethical Considerations: Generative AI can produce deep fakes, plagiarized content, or other deceptive media. Hence, ethical usage and detection methods are important aspects of this field.

Though these principles constitutes the backbone of Generative AI.??As technology evolves, new principles and best practices continues to emerge